Statistical quality assessment of fingerprints

ABSTRACT

The present invention relates generally to human fingerprints. More specifically the present invention relates to assessing the quality of a fingerprint image. An assessment is made by analyzing characteristics of image sub-regions. For example, the characteristics may include statistics that are associated with a sub-region. If a sub-region is found to have unacceptable characteristics, it can be modified through a morphological operation to compensate for the unacceptable characteristics.

RELATED APPLICATION DATA

This application claims the benefit of U.S. Provisional PatentApplication Nos. 60/493,690, filed Aug. 7, 2003, and 60/557,856, filedMar. 26, 2004. This application is also related to the following U.S.Pat. Nos.: 5,841,886, 6,343,138 and 6,389,151 and assignee's U.S. patentapplication Ser. No. 10/893,141, filed Jul. 15, 2004. Each of the abovepatent documents is herein incorporated by reference.

TECHNICAL FIELD

The present invention relates generally to fingerprints (e.g., humanfingerprints). One aspect of the invention assesses the quality offingerprints. Another aspect of the invention embeds so-calledfingerprint minutiae with a digital watermark.

BACKGROUND AND SUMMARY

Biometrics is a science that can be used to measure and analyzephysiological characteristics, such as fingerprints, eye retinas andirises, facial patterns and hand geometry. Some biometrics technologiesinvolve measurement and analysis of behavioral characteristics, such asvoice patterns, signatures, and typing patterns. Because biometrics,especially physiological-based technologies, measures qualities that anindividual usually cannot change, it can be especially effective forauthentication and identification purposes.

Fingerprint-based identification is one of the oldest successfulbiometric-based identification methods. Each person has a set of unique,typically immutable fingerprints. A fingerprint includes a series ofridges and valleys (or “furrows”) on the surface of a finger. Theuniqueness of a fingerprint can be determined by a pattern of ridges andfurrows, as well as minutiae points. Minutiae points are local ridgecharacteristics that generally occur at either a ridge bifurcation or ata ridge ending.

Fingerprint matching techniques can be placed into two generalcategories: minutiae-based and correlation-based matching.Minutiae-based techniques first find minutiae points and then map theirrelative placement on the finger. Each minutiae point may include aplacement location (e.g., an x, y coordinate in an image or spatialdomain) and a directional angle. (The curious reader is directed to,e.g., U.S. Pat. Nos. 3,859,633 and 3,893,080, both to Ho et al., whichdiscuss fingerprint identification based upon fingerprint minutiaematching. Each of these patent documents is herein incorporated byreference.) The National Institute of Standards and Technology (NIST)distributes public domain software for fingerprint analysis. Thesoftware is available from the Image Group at NIST under the name NISTFINGERPRINT IMAGE SOFTWARE (NFIS), which includes a minutiae detectorcalled, MINDTCT. MINDTCT automatically locates and records ridge endingand bifurcations in a fingerprint image (e.g., identifies minutiaelocations). NFIS also includes a pattern classification module calledPCASYS.

Correlation techniques correlate normalized versions of fingerprintimages to determine if a first fingerprint image (control) matches asecond fingerprint image (sample). (The curious reader is direction to,e.g., U.S. Pat. Nos. 6,134,340 and 5,067,162, which discuss correlationtechniques even further. Each of these patent documents is hereinincorporated by reference.).

Other fingerprinting efforts have focused on locating or analyzing theso-called fingerprint “core”. U.S. Pat. No. 5,040,224 to Hara disclosesan approach for preprocessing fingerprints to correctly determine aposition of a core of each fingerprint image for later matching byminutiae patterns. U.S. Pat. No. 5,140,642 to Hsu et al. is directed toa method for determining the actual position of a core point of afingerprint based upon finding ridge flows and assigning a directioncode, correcting the ridge flows, and allocating the core point basedupon the corrected direction codes. Each of these patents is hereinincorporated by reference.

Despite the work in the prior art, there are still problems to besolved, and improvements to be made. For example, quality of an originalfingerprint image can be poor—due to imaging issues or physicalconditions (e.g., wetness, dryness, etc.) when sampling a fingerprint.When fingerprint quality is poor, the print may contain local ridgepattern distortion, which may result in an incorrect analysis of thefingerprint.

Accordingly, one inventive aspect of the invention provides a method toassess the quality of fingerprint images using local statistics of acaptured fingerprint. Assessing the quality of a fingerprint is vital,e.g., to determine whether a fingerprint should be recaptured or whethera fingerprint image should be modified or enhanced.

This disclosure also includes systems and methods for hiding fingerprintminutiae information in a photographic image (e.g., a photograph carriedby an ID document). The fingerprint minutiae information is representedas a so-called digital watermark component.

Digital watermarking is a process for modifying physical media orelectronic signals to embed a machine-readable code into the media orsignals. The media or signals may be modified such that the embeddedcode is imperceptible or nearly imperceptible to the user, yet may bedetected through an automated detection process.

Digital watermarking systems typically have two primary components: anencoder that embeds the watermark in a host signal, and a decoder thatdetects and reads the embedded watermark from a signal suspected ofcontaining a watermark (a suspect signal). The encoder embeds awatermark by altering the host signal. The reading component analyzes asuspect signal to detect whether a watermark is present. In applicationswhere the watermark encodes information, the reader extracts thisinformation from the detected watermark. Several particular watermarkingtechniques have been developed. The reader is presumed to be familiarwith the literature in this field. Some techniques for embedding anddetecting imperceptible watermarks in media signals are detailed inassignee's U.S. Pat. Nos. 5,862,260 and 6,614,914. Each of these patentdocuments is herein incorporated by reference.

Further features and advantages will become even more apparent withreference to the following detailed description and accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating fingerprint capture.

FIG. 2 a is a diagram illustrating quality assessment for fingerprints.

FIG. 2 b is a diagram illustrating quality assessment for fingerprintsincluding an enhancement module.

FIG. 3 is a diagram illustrating ridge and valley thickness in afingerprint image block.

FIG. 4 is a diagram illustrating an alternative method for qualityassessment of fingerprints.

FIGS. 5 a-5 c show typical Gaussian distributions for fingerprint imageblocks having three different qualities, i.e., good, wet and dry,respectively.

FIGS. 6 and 7 each illustrate two different fingerprint images, witheach image having a different population density of poor image blocks,while each image includes about the same number of poor image blocks.

FIG. 8 illustrates results of an image enhancement technique.

FIG. 9 illustrates an identification document.

FIG. 10 illustrates a cross-sectional view of the FIG. 9 identificationdocument.

FIG. 11 illustrates a method for embedding fingerprint minutiae datainto a photographic (or other) image for placement on an identificationdocument.

FIG. 12 illustrates a method for validating the identification documentshown in FIG. 11.

FIGS. 13 a-13 f illustrate a method for minutiae-matching.

FIGS. 14 a-14 e illustrate an alternative method for minutiae-matching.

Appendix A FIGS. 1 a-1 d illustrate, respectively, a fingerprint image,a global directional map, a block orientation and a measured thickness.

Appendix A FIGS. 2 a-2 c illustrate for a good fingerprint,respectively, a fingerprint image, a rotated black/white image, and aGaussian PDF of line thickness.

Appendix A FIGS. 3 a-3 c illustrate for a wet fingerprint, respectively,a fingerprint image, a rotated black/white image and a Gaussian PDF ofline thickness.

Appendix A FIGS. 4 a-4 c illustrate for a dry fingerprint, respectively,a fingerprint image, a rotated black/white image, and a Gaussian PDF ofline thickness.

Appendix A FIGS. 5 a-5 c illustrate, respectively, good, dry and wetfingerprints, while Appendix A FIGS. 5 d-5 f illustrates, respectively,detected poor quality blocks for the fingerprints shown in FIGS. 5 a-5c.

Appendix A FIGS. 6 a-6 d illustrates detected dray blocks before andafter morphology; specifically, FIG. 6 a shows a fingerprint with dryblocks without morphology; FIG. 6 b shows a number of dry block in theFIG. 6 a fingerprint after morphology; FIG. 6 c shows a close up of adry block; and FIG. 6 d shows a close up of the FIG. 6 c block aftermorphology.

Appendix A FIGS. 7 a-7 b show a Dry index and a corresponding ClusterIndex.

Appendix A FIGS. 8 a-8 c illustrate, respectively, a fingerprint image,the number of detected dry blocks within the image, and a group of dryblocks marked with a square.

Appendix A FIGS. 9 a-9 c illustrate, respectively, a fingerprint image,the number of detected dry blocks and a group of dry blocks marked witha squares.

DETAILED DESCRIPTION

Statistical Quality Assessment of Fingerprints

This section provides methods and systems to assess the quality of afingerprint image using characteristics associated with the image orwith the underlying fingerprint. In one implementation thecharacteristics include local statistics. By using the term “localstatistics” we generally mean those statistics that are attributable toa particular image sub-region, like a 24-by-24 pixel block or 32-by-16pixel block, etc., or statistics that are attributable to a group ofimage sub-regions. Determining quality of a fingerprint image issignificant for a number of reasons. For example, a referencefingerprint image, e.g., which is stored in a database, may not bereadily matched against a sample fingerprint if either have poor qualityor distortion. Poor quality or distortion may be caused from a number ofproblems including, e.g., a fingerprint being too “wet” or too “dry”,image scanner distortion, too much ink, etc.

Fingerprint capture is discussed with reference to FIG. 1. A humansubject presents her finger to be sampled (e.g., the subject is“fingerprinted”). It should be appreciated that the term “fingerprint”is used in its broadest context in this patent document, so as toinclude fingerprints, thumbprints and even palm prints. A fingerprintreader or scanner captures an image of the subject's fingerprint.Suitable fingerprint readers are provided by a number of companies,including Indentix, headquarter in Minnetonka, Minn., USA, under theproduct names of DFR® Professional Direct Fingerprint Readers (e.g., DFR90, DFR 2080 and DFR 2090). Specifications for the DFR 2090 aresummarized in Table 1, below. Of course, there are many otherfingerprint readers that can be suitably interchanged with this aspectof the invention. A fingerprint reader often provides a fingerprintimage, e.g., a 512×512 pixel image.

TABLE 1 DFR 2090 Specifications: Features: Comments or Values:Resolution 500 dpi × 500 dpi +/− 3%, when sampled at 12.2727 MHz, GrayScale 256 shades of gray Platen Active Area 0.8 × 1.0 inches (20 mm × 25mm) Output USB Version 1.1, 12 Mbps transfer rate; USB Connector: Type AFemale Analog Output+: RS-170, BNC connector Analog Output Signal: 0.7volt p-p +/− 0.05 v into 75 ohm termination Distortion less than 1.0%trapezoid

(As an alternative fingerprint image capture method, a human subjectinks her finger and then “rolls” her inked finger over asubstrate—transferring her fingerprint to the substrate via the ink. Anoptical reader (e.g., a flatbed scanner) images the inked substrate tocapture an image of the rolled fingerprint.).

We assess the quality of a captured fingerprint image. Our preferredmethod and system analyzes statistical properties in a local fingerprintarea, which involves, e.g., the average and spread (i.e., standarddeviation) of the thickness of both fingerprint ridges and fingerprintvalleys. In FIG. 1, a “ridge” is shown as black, and a “valley” betweentwo ridges is represented by white space or as a void. Thickness can bemeasured in terms of pixels or in terms of a predetermined unit.

With reference to FIG. 2 a, captured fingerprint data is optionallysegmented (step 21) and enhanced (step 22). These steps, while notnecessary to practice the invention, help reduce image noise. Forexample, the segmentation step 21 removes unwanted background noise andclutter (e.g., removes noise introduced by an optical reader) from thefingerprint itself. The enhancement (e.g., a non-linear, medium filter)can also remove noise. Ridge orientation for the fingerprint image isdetermined in step 23. (In some implementations, we obtain the ridgeorientation from NIST's NFIS software, particularly the “rors( )”function in the PCASYS module. In other implementations we perform apattern recognition or orientation analysis to determine whether thefingerprint image includes distortion.). The ridge orientation gives anindication of whether the fingerprint image includes geometricdistortion (e.g., rotation, scale, skew, etc.). This indication can beused to compensate for such distortion, if needed. A local fingerprintimage area (e.g., 24-by-24 pixel block, etc.) is selected for analysisin step 24. This selected image block area is also referred to as a“selected block.”

We preferably perform a detailed orientation analysis for the selectedimage block in step 25. This detailed orientation step determinesdistortion that may be specific to or localized in the selected localimage block. For example, a lower left image area may have moredistortion than an upper right image area. (The NFIS software provides adetail orientation indication, i.e., the “Dft_dir_powers( )′ function inthe MINDTCT module. Of course, other techniques can be suitableinterchanged with this aspect of the invention to determine a localdetail orientation, e.g., area comparison or orientation metrics, etc.)Geometric registration is performed in steps 26. For example, theselected image block is rotated to compensate for the global distortionidentified in step 23 and/or adjusted to compensate the localizeddistortion identified in step 25. Each local block is preferablyadjusted or rotated so that lines are scanned about normal to the ridgeorientation. (We note that this registration or adjustment step 26alternatively can be performed in multiple stages, with, e.g., globaldistortion being compensated for prior to block selection or after blockselection but prior to localized block compensation. Alternatively, theglobal and detail indicators (e.g., rotation angles) can be combined toyield a compensation metric, which is used to adjust a selected imageblock.)

After image adjustment, we determine ridge and valley characteristicsfor the selected block in step 27. In one implementation we measure thethickness (or width) of ridges and valleys, e.g., in terms of pixels perhorizontal scan line. (We can distinguish between a ridge and valleybased on color or grayscale level. For example, in a bitonal image,ridges are black and valleys are white. The number of pixels for a ridge(black pixels) is counted and the numbers of pixels for valleys (whitepixels) are counted. In the case of a grayscale image, we threshold thegrayscale pixel values so a ridge is identified as a ridge if the ridgeincludes a grayscale value at or above a predetermined grayscale value(e.g., grayscale value >128). In still another implementation webinarize the grayscale image to classify a ridge or valley. A thresholdvalue for binarization can be an average value in a local block.).

With reference to FIG. 3, we show a simplified example of an8-by-8-pixel block including two ridges R1 and R2 (shown with hashmarks) and one valley V1 (white or void space). The dotted lines acrossthe top of the block are provided to show a separation betweenindividual pixels. For simplicity, the widths of only two scan lines areconsidered, i.e., along scan lines AA and BB. Along the AA line, ridgeR1 includes a length of 3 pixels, while ridge R2 includes a length of 2pixels. Valley V1 is 1 pixel wide along the AA line. Along the BB line,each ridge R1 and R2 is 2 pixels wide, and valley V1 is 2 pixels wide.Of course, this example is not meant to be limiting. In practice, thenumber of scan lines will vary, e.g., up to one scan per pixel row, theblock size will vary even perhaps to include irregular selected imageareas and the number and widths of the ridges and valleys will certainlyvary according to individual biometric characteristics. (We note thatthe FIG. 3 representation includes two partial valleys PV1 and PV2 alongthe block edges. We preferably exclude partial ridges and partialvalleys from local block analysis. However, in alternativeimplementations, partial ridges and/or valleys are considered andaccounted for when determining local block statistics. In some of thesealternative implementations we measure partial ridges and valleys andadd that measurement to a neighboring block. For example, we add themeasurements from a partial valley or ridge to measurements from acorresponding partial ridge or valley in a neighboring block. Or apartial ridge or valley can be weighted so as to be less significant ascompared to a complete ridge or valley. In other implementation weselect a block or image area based on ridge or valley boundaries,ensuring that a block does not include partial ridges or partialvalleys.)

Returning to FIG. 2 a, once the ridge and valley characteristics aredetermined for a selected image block, we determine local statistics(step 28) for the selected blocks based on the characteristics (e.g.,ridge and valley widths and/or relationships). For example, we calculatethe average width of both the ridges and valleys, and the spread (i.e.,standard deviation, or “σ”) of both the ridges and valleys. Spreadprovides an indication of the regularity of ridges and valleys. Usingthe example shown in FIG. 3, the average width (along the scan lines AAand BB) of the ridges is 2.25 pixels and the average width (along thescan lines AA and BB) of the valley is 1.67 pixels. The standarddeviation for the ridges is 0.5, while the standard deviation of thevalley is 0.41.

The local statistics are used to determine a classification for aselected block (step 29). For example, the block can be classified as,e.g., good, marginal and poor. Or the block can be classified as good(or acceptable), wet and dry. A wet fingerprint, where the ridges aredominant, typically includes an average ridge thickness (μ_(b)) that islarger than the valley thickness (μ_(w)), and vice versa in case of adry fingerprint. We have found that a good fingerprint image (e.g., FIG.5 a) tends to have small standard deviation values for thickness forboth ridges and valleys. If a fingerprint is wet (FIG. 5 b), the mean ofridges (dotted line) is larger than that of the valleys (solid line).FIG. 5 b also shows that for a typical wet fingerprint, the standarddeviation of the ridges is relatively large. The opposite result isshown if a fingerprint is dry (see FIG. 5 c—where the dotted linecorresponds with rides and solid line corresponds with valleys). Ofcourse other statistical factors can be used when determining aclassification of a selected block. In one implementation we use thefollowing statistics to classify or assess blocks:

To classify a block as a dry block: (standard deviation of valley>1.5pixels) & (mean of valley>mean of ridge);

To classify a block as a wet block: (standard deviation of ridge>1.5pixels) & (mean of valley<mean of ridge); and

Otherwise the block can be classified as acceptable.

Of course, these classification values and relationships can be variedin different implementations according to accuracy needed.

We cache (or otherwise remember) the quality assessment for the selectedblock. In some implementations we associate the quality assessment witha spatial image location. For example, we create a spatial map ofquality assessments for a fingerprint image.

Steps 25 through 29 are repeated for each block of the fingerprintimage. (In an alternative implementation, we repeat the process only fora subset of image blocks, but not for all of the blocks. Thepredetermined number should be selected to ensure a high level ofconfidence when making a quality decision for a fingerprint image, orthe selected blocks should correspond to a predetermined area like acore of a fingerprint.).

A global assessment regarding the quality of the fingerprint image ismade in step 30. The global assessment is the arbitrator of whether theoverall quality of the fingerprint image is, e.g., good, bad or poor orotherwise. In a first case, we add up the number low quality blocks (asdetermined, e.g., in step 29). If the number is over a predeterminedthreshold, the image is considered low quality. In a related case, weadd up the number of acceptable quality blocks, and if the number isabove (or below) a predetermined threshold we make a decision regardingthe overall quality of the fingerprint image.

In an alternative global quality assessment, population density of poorblocks (or good blocks) is considered. Recall that we discussed abovethat in some implementations we track or record the spatial location ofblocks in terms of their quality. So if the number of poor qualityblocks would otherwise disqualify a fingerprint image due to poorquality, but the poor quality blocks are reasonably dispersed throughoutthe fingerprint image, the image may be otherwise accepted. In thisalternative arrangement, taking into consideration both the number ofpoor block and the density of the poor blocks determines global qualityof a fingerprint image. Thus a fingerprint image having poor blocks thatare locally condensed may yield a lower global assessment in comparisonto an image including poor blocks dispersed throughout the image. Tofurther illustrate, FIGS. 6 and 7 have the same number of poor blocks,but are arranged in different density patterns. FIG. 6 includes acondensed population of dry blocks in the lower right portion of thefingerprint image. In comparison, the fingerprint in FIG. 7 has its dryblocks somewhat dispersed through out the image. The FIG. 7 fingerprintmay have a better global assessment when compared to the FIG. 6fingerprint. (Poor block population can be measured in terms of pixeldistance measured, e.g., by Euclidean distances between groups of poorblocks, etc.)

FIG. 4 provides a functional block diagram showing one implementation ofour quality assessment invention. Fingerprint image data is provided foranalysis. Ridge orientation is determined to help compensate forgeometric distortion (e.g., image rotation). As discussed with respectto other implementations, this orientation may include both global andlocalized distortion assessments. A selected block is rotated orotherwise adjusted to compensate for the determined distortion. Ridgeand valley characteristics are determined, and local statistics (e.g.,standard deviation and average width of valleys and ridges) aregenerated. The local statistics are accumulated for selected blocks, anda global quality assessment for a fingerprint image is determined basedon the accumulation.

With reference to FIG. 2 b we provide an enhancement module to modifylocal blocks that are classified as being poor. For example, selectedlocal blocks that are classified as dry or wet are modified using amorphological technique. This technique reduces or expands the width ofvalleys (or ridges). An erosion method is used to reduce, and a dilationmethod is used for expanding. In a simplistic example, a MatLab®function “erode” (or “thin”) is used to reduce a valley or ridge, and aMatLab® function “dilate” (or “thicken”) is used to grow a valley orridge. Of course, other functions, programs and techniques that erode orgrow ridges and valleys can be suitably interchanged with this aspect ofthe invention. (For simplicity of illustration, the FIG. 8 example onlymorphs dry blocks in the FIG. 8(i) image. FIG. 8(iii) shows the numberof dry blocks is reduced in comparison to FIG. 8(ii), while the numberof wet blocks remains the same as before (since the morphology operationwas only performed on dry blocks). The detailed views of before andafter operations are shown in FIG. 8 (iv) and (v).).

After performing the morphological operation (step 29 a), the imageblock is preferably reevaluated. For example, flow continues back tostep 27 where the ridge and valley characteristics are redetermined. (Weoptionally place a limiting function in the FIG. 2 b implementation todetermine or limit the number of times the morphological operation canbe performed on a selected block, to help preserve image integrity andreduce processing time. A quality assessment of the last loop can beused when the limit is reached.).

Alternatives

In alternative implementations we adaptively select a block size toimprove our quality assessment. For example, we examine a neighborhoodof blocks or pixels, and based on a neighborhood metric (e.g., generalassessment) we adaptively enlarge or decrease block size. Also, in somealternative implementations we determine which blocks correspond to thefingerprint's core, and we weight (or assign more value to) the core'sblock quality assessments so as to more significantly affect the globalassessment. We can also use a frequency-based analysis of blockcharacteristics (e.g., ridges and valleys) to weight or influence theglobal assessment.

Applications

We envision that our quality assessment techniques will be used incombination with identification document issuance, identificationdocuments and biometric capture stations. For example, in identificationdocument issuance processes, biometric data (e.g., fingerprints) arecollected and perhaps replicated on the document (e.g., an image of thefingerprint is printed on the document), stored in electronic circuitryor optical memory of the identification document (e.g., smart card),printed in a machine-readable format like a 2D barcode or conveyedthrough a digital watermark, or stored in a database. Our qualityassessment techniques can be used to ensure the quality of fingerprintsintroduced into such a process.

Even more information regarding some types of identification documentsis provided below. Of course, the inventive techniques will improveother types of identification documents as well.

We also envision that our techniques can be used in combination with theminutiae hiding techniques discussed below. For example, we can use ourquality assessment techniques as a fingerprint pre-processing step,which ensure the quality of an image prior to minutiae mining andembedding.

Identification Documents

Identification documents (hereafter “ID documents”) play a critical rolein today's society. One example of an ID document is an identificationcard (“ID card”). ID documents are used on a daily basis—to proveidentity, to verify age, to access a secure area, to evidence drivingprivileges, to cash a check, and so on. Airplane passengers are requiredto show an ID document during check in, security screening, and/or priorto boarding their flight. In addition, because we live in anever-evolving cashless society, ID documents are used to make payments,access an ATM, debit an account, or make a payment, etc.

Many types of identification cards and documents, such as drivinglicenses, national or government identification cards, bank cards,credit cards, controlled access cards and smart cards, carry thereoncertain items of information which relate to the identity of the bearer.Examples of such information include name, address, birth date,signature and photographic image; the cards or documents may in additioncarry other variant data (i.e., data specific to a particular card ordocument, for example an employee number) and invariant data (i.e., datacommon to a large number of cards, for example the name of an employer).All of the cards and documents described above will hereinafter begenerically referred to as “ID documents” or “identification documents”.

In the production of images useful in the field of identificationdocumentation, it is oftentimes desirable to embody into a document(such as an ID card, drivers license, passport or the like) data orindicia representative of the document issuer (e.g., an official seal,or the name or mark of a company or educational institution) and data orindicia representative of the document bearer (e.g., a photographiclikeness, name or address). Typically, a pattern, logo or otherdistinctive marking representative of the document issuer will serve asa means of verifying the authenticity, genuineness or valid issuance ofthe document. A photographic likeness or other data or indicia personalto the bearer will validate the right of access to certain facilities orthe prior authorization to engage in commercial transactions andactivities.

Commercial systems for issuing ID documents are of two main types,namely so-called “central” issue (CI), and so-called “on-the-spot” or“over-the-counter” (OTC) issue.

CI type ID documents are not immediately provided to the bearer, but arelater issued to the bearer from a central location. For example, in onetype of CI environment, a bearer reports to a document station wheredata is collected, the data are forwarded to a central location wherethe card is produced, and the card is forwarded to the bearer, often bymail. Another illustrative example of a CI assembling process occurs ina setting where a driver passes a driving test, but then receives herlicense in the mail from a CI facility a short time later. Still anotherillustrative example of a CI assembling process occurs in a settingwhere a driver renews her license by mail or over the Internet, thenreceives a drivers license card through the mail.

Centrally issued identification documents can be produced from digitallystored information and generally comprise an opaque core material (alsoreferred to as “substrate”), such as paper or plastic, sandwichedbetween two layers of clear plastic laminate, such as polyester, toprotect the aforementioned items of information from wear, exposure tothe elements and tampering. The materials used in such CI identificationdocuments can offer the ultimate in durability. In addition, centrallyissued digital identification documents generally offer a higher levelof security than OTC identification documents because they offer theability to pre-print the core of the central issue document withsecurity features such as “micro-printing”, ultra-violet securityfeatures, security indicia and other features currently unique tocentrally issued identification documents. Another security advantagewith centrally issued documents is that the security features and/orsecured materials used to make those features are centrally located,reducing the chances of loss or theft (as compared to having securedmaterials dispersed over a wide number of “on the spot” locations).

In addition, a CI assembling process can be more of a bulk processfacility, in which many cards are produced in a centralized facility,one after another. The CI facility may, for example, process thousandsof cards in a continuous manner. Because the processing occurs in bulk,CI can have an increase in efficiency as compared to some OTC processes,especially those OTC processes that run intermittently. Thus, CIprocesses can sometimes have a lower cost per ID document, if a largevolume of ID documents is manufactured.

In contrast to CI identification documents, OTC identification documentsare issued immediately to a bearer who is present at a document-issuingstation. An OTC assembling process provides an ID document“on-the-spot”. (An illustrative example of an OTC assembling process isa Department of Motor Vehicles (“DMV”) setting where a driver's licenseis issued to person, on the spot, after a successful exam.). In someinstances, the very nature of the OTC assembling process results insmall, sometimes compact, printing and card assemblers for printing theID document.

OTC identification documents of the types mentioned above can take anumber of forms, depending on cost and desired features. Some OTC IDdocuments comprise highly plasticized polyvinyl chloride (PVC) or have acomposite structure with polyester laminated to 0.5-2.0 mil (13-51.mu.m) PVC film, which provides a suitable receiving layer for heattransferable dyes which form a photographic image, together with anyvariant or invariant data required for the identification of the bearer.These data are subsequently protected to varying degrees by clear, thin(0.125-0.250 mil, 3-6 .mu.m) overlay patches applied at the print head,holographic hot stamp foils (0.125-0.250 mil 3-6 .mu.m), or a clearpolyester laminate (0.5-10 mil, 13-254 .mu.m) supporting common securityfeatures. These last two types of protective foil or laminate sometimesare applied at a laminating station separate from the print head. Thechoice of laminate dictates the degree of durability and securityimparted to the system in protecting the image and other data.

FIGS. 9 and 10 illustrate a front view and cross-sectional view (takenalong the A-A line), respectively, of an example identification (ID)document 40. Our discussion of a particular type of identificationdocument is not meant to be limiting. Rather, our inventive techniqueswill apply to many different types of identification documents, systemsand processes. In FIG. 9, the ID document 40 includes a photographicimage 42, a bar code 44 (which may contain information specific to theperson whose image appears in photographic image 42 and/or informationthat is the same from ID document to ID document), variable personalinformation 46, such as an address, signature, and/or birth date, andbiometric information 48 associated with the person whose image appearsin photographic image 42 (e.g., a fingerprint). Although not illustratedin FIG. 9, the ID document 40 can include a magnetic stripe or opticalmemory surface (which, for example, can be on the rear side (not shown)of the ID document 40), and various security features, such as asecurity pattern (for example, a printed pattern comprising a tightlyprinted pattern of finely divided printed and unprinted areas in closeproximity to each other, such as a fine-line printed security pattern asis used in the printing of banknote paper, stock certificates, and thelike).

Referring to FIG. 10, the ID document 40 comprises a pre-printed core 50(such as, for example, white polyvinyl chloride (PVC) material) that is,for example, about 25 mil thick. The core 50 is laminated with atransparent material, such as clear PVC material 52, which, by way ofexample, can be about 1-5 mil thick. The composite of the core 50 andclear PVC material 52 form a so-called “card blank” 55 that can be up toabout 30 mils thick. Information 56 a-c is printed on the card blank 55using a method such as Dye Diffusion Thermal Transfer (“D2T2”) printing(described further in U.S. Pat. No. 6,066,594, which is incorporatedhereto by reference.) The information 56 a-c can, for example, comprisean indicium or indicia, such as the invariant or nonvarying informationcommon to a large number of identification documents, for example thename and logo of the organization issuing the documents. In someimplementations the information 56 a-c includes a digital watermark,perhaps carrying watermark minutiae information as discussed below. Theinformation 56 a-c may be formed by any known process capable of formingthe indicium on the specific core material used.

To protect the information 56 a-c that is printed, an additional layerof overlaminate 54 can be coupled to the card blank 55 and printing 56a-c using, for example, 1 mil of adhesive (not shown). The overlaminate54 can be substantially transparent. Materials suitable for forming suchprotective layers are known to those skilled in the art of makingidentification documents and any of the conventional materials may beused provided they have sufficient transparency. Examples of usablematerials for overlaminates include biaxially oriented polyester orother optically clear durable plastic film.

Because ID document 40 can be used to enable and facilitate personalidentification, it often is desirable to manufacture the ID document 40in a manner to deter counterfeiting and/or fraudulent alteration. Thereare a number of known ways to increase the security of ID documents 40,including methods that incorporate additional information or securityfeatures and methods that adapt existing information on the card to helpprevent or make evident fraud. For example, numerous types oflaminations have been employed in which the information-bearing surfaceis heat or solvent-laminated to a transparent surface. The materials forand the process of lamination are selected such that if an attempt ismade to uncover the information-bearing surface for amendment thereof,the surface is destroyed, defaced or otherwise rendered apparent theattempted intrusion.

Conveying Fingerprint Minutiae with Digital Watermarks

We have developed a method and system to improve authentication andvalidation techniques using fingerprints (i.e., human fingerprints). Ourinventive techniques are readily applied to identification documents andbiometric systems, e.g., biometric-controlled access, databaseverification, etc., etc., which use fingerprints for authentication orvalidation. In one implementation, we validate an identificationdocument such as a drivers license, passport, photo ID, visa, credit orbank card, security card, national identification document, voterregistration card or document, immigration document, permit,certificate, employment badge, secure access card or document, etc.,etc. Of course, some of these identification documents may also includeelectronic circuitry, e.g., a smart card. (See, e.g., assignee's U.S.patent application Ser. No. 09/923,762 (published as US 2002-0080994A1), Ser. No. 10/282,908 (published as US 20030128862 A1), and Ser. No.10/465,769 (published as US 2003-0210805 A1) for additional examples ofhow digital watermarks can interact with smart cards. Each of thesepatent documents is herein incorporated by reference.) Otheridentification documents include so-called optical memory, e.g., aLaserCard provided by LaserCard Systems Corporation in Mountain View,Calif., or magnetic memory. These types of documents are interchangeablyreferred to as “ID documents” or “identification documents.” Ourtechniques can also be used to determine whether a bearer of an IDdocument is an authorized bearer of the ID document.

Our inventive techniques match fingerprint characteristics between acontrol fingerprint and a sample fingerprint. A control fingerprint, ordata representing a control fingerprint or a subset of the controlfingerprint, can be stored or carried by an identification document. Forexample, a control fingerprint can be printed on an identificationdocument or can be stored in electronic or optical/magnetic memory. Inmore preferred implementations, we steganographically hide fingerprintdata on or in an ID document. Our preferred form of steganography isdigital watermarking.

Fingerprint characteristics take many forms, and our comparison betweena control and sample fingerprint may use different types ofcharacteristics. To simplify the discussion, however, we focus onfingerprint minutiae. As discussed above, fingerprint minutiae refer tofeature points usually identified at ridge endings and bifurcations in afingerprint. Each minutiae point generally includes a placement location(e.g., an x,y spatial or image placement coordinate) and a directionalangle.

One prior work involving human fingerprints and digital watermarks is“Hiding Fingerprint Minutiae in Images,” Jain and Uludag in: Proc. ofThird Workshop on Automatic Identification Advanced Technologies(AutoID), pp. 97-102, Tarrytown, N.Y., Mar. 14-15, 2002, which is hereinincorporated by reference (hereafter “Jain”). Jain contemplatesembedding a minutiae point's x coordinate, y coordinate, and directionas a 9-bit number, for a total of 27 bits per minutiae point. Jainproposes to embed approximately 25 minutiae points in an image (roughly675 bits), although a typical fingerprint has 100-200 minutiae.

Jain's technique requires a large payload capacity watermark. Such alarge payload may impose practicality issues, e.g., resulting invisibility artifacts, given real world constraints when manufacturing IDdocuments.

Our approach, in contrast to Jain's, embeds fingerprint minutiae data(e.g., minutiae point locations and perhaps a direction indicator)explicitly as a digital watermark component. The minutiae data can beembedded in a photographic image, background, graphic, ghost image,seal, data or biometric data stored in optical or electronic memory. Ina preferred implementation the minutiae data is conveyed in a mannersimilar to a so-called digital watermark orientation component. Theexplicit mapping of minutiae points, once decoded from the digitalwatermark, can be compared to a sample fingerprint for authentication orvalidation.

For more details on embedding an image watermark, and detecting andreading the image watermark from a digitized version of the image afterprinting and scanning see, e.g., assignee's U.S. Pat. Nos. 5,862,260 and6,614,914, which are each herein incorporated by reference.

In order to make a watermark more robust to geometric distortion (e.g.,scaling, rotation, etc.), a watermark may include an orientationwatermark signal component. Together, the watermark message signal andthe orientation watermark signal form the watermark signal.

One type of watermark orientation signal is an image signal thatcomprises a set of impulse functions in a transform domain, like aFourier magnitude domain, e.g., each with pseudorandom phase. To detectrotation and scale of a watermarked image (e.g., after printing andscanning of the watermarked image), a watermark decoder converts thewatermarked image to the Fourier magnitude domain and then performs,e.g., a log polar resampling of the Fourier magnitude image. Ageneralized matched filter correlates a known orientation signal withthe re-sampled watermarked signal to find the rotation and scaleparameters providing the highest correlation. The watermark decoderperforms additional correlation operations between the phase informationof the known orientation signal and the watermarked signal to determinetranslation parameters, which identify the origin of the watermarkmessage signal. Having determined the rotation, scale and translation ofthe watermark signal, the reader then adjusts the image data tocompensate for this distortion, and extracts the watermark messagesignal, if any.

We combine fingerprint minutiae point locations (e.g., a minutiaemapping or “grid” of such locations) with a watermark orientationsignal. This minutiae mapping can be referred to as a “minutiae map” or“minutiae grid,” which are used interchangeably in this patent document.An embedded minutiae grid can be recovered from an image signal. In somecases, a watermark orientation signal, which is also embedded in theimage signal, is used to synchronize or register the image signal (e.g.,compensate for scaling, rotation, and translation of the image signal).While we discuss, below, adding a minutiae data to an image in a spatialdomain, our invention is not so limited. Explicit minutiae grid pointscan be added to an image signal in a transform domain as well.

Our techniques improve ID document security. With reference to FIG. 11,a control fingerprint is harvested. For example, a human subjectpresents her fingerprint for fingerprinting in step 90. A fingerprintreader (e.g., like the DFR® 2090 discussed above) can be used to capturean image of the subject's fingerprint, or a previously capturedfingerprint, which belongs the human subject, can be analyzed. NIST'sNFIS software, which includes a minutiae detector called, MINDTCT,provides acceptable minutiae information in step 92. Of course, othersoftware and/or techniques for determining minutiae information can besuitably interchanged with the MINDTCT module. While the minutiaeinformation is represented graphically in FIG. 11, it need not be so.

The minutiae information is mapped or transformed into a minutiae domain(e.g., an intermediate domain which represents minutiae placement orlocations). To illustrate, the minutiae information as identified instep 92 may be organized relative to a 512×512 pixel image. A mappingmaps the minutiae points to, e g., a 128×128 pixel area or to a locationrepresentation. Of course, the minutiae points can be mapped to largeror smaller areas as well. A minutiae map or grid shown in step 94corresponds to such a mapping. In one implementation, to represent adirection angle of each minutiae point or a subset of minutiae points, aminutiae point's direction angle is encoded or assigned a pseudo-randomphase. For example, a minutiae point is represented as a peak signal,with each peak including a pseudo-random phase relative to other peaks.In other implementations minutiae point orientation is disregarded.

A representation corresponding to a minutiae map is to be explicitlyembedded via a digital watermark. Remember, above, that some watermarkorientation signals include transform domain characteristics. So in oneimplementation, a minutiae map or minutiae domain is viewed as if itwere a representation of (or a corresponding partner to) a transformdomain characteristic, e.g., the minutiae domain is viewed as atransform domain specification. The minutiae map or minutiae domain isthus transformed, e.g., by an inverse fast Fourier transform (IFFT) instep 96. (Transforming the minutiae map allows for a single minutiaepoint to be transformed or spread to a large number, or possibly all, ofthe points in the transformed minutiae domain. Otherwise, embedding ofthe actual points as spatial points may result in unwanted visualartifacts and less robust embedding.). The transformed minutiae map ispreferably permuted or scrambled (step 98), yielding a final spatialdomain representation of the minutiae map 94. For example, thetransformed minutiae map is scrambled using a known cryptographic key oralgorithm, to provide security and/or interference suppression betweenthe transformed minutiae map and a watermark orientation component. Thepermuted, transformed minutiae map is combined (step 102) with awatermark orientation component (item 100).

A photographic image corresponding to the human subject is provided(e.g., the unmarked image 104). A digital camera can directly capturethe photographic image 104, or, alternatively, the photographic image104 can be provided from a photographic data repository or from an imagescanner, which optically captures an image from a printed photograph.The combined orientation component/minutiae information (sometimescollectively referred to as “watermark minutiae information”) issteganographically embedded or combined 106 in the unmarked photographicimage 104 to yield a marked photographic image 108. In someimplementations the watermark minutiae information is redundantlyembedded across the photographic image 104. In other implementations atransformed permuted minutiae map is tiled or redundantly embedded, witheach instance of a transformed permuted minutiae map being representeddifferently according to a key (e.g., as introduced via block 98). Theembedded photograph 108 is then printed on an ID document 110, or insome cases is stored in electronic or optical/magnetic memory of an IDdocument. The resulting ID document 110 includes information (e.g.,watermark minutiae information) for validation.

Instead of embedding the watermark minutiae information in aphotographic image, the watermark minutiae information can be embeddedin a graphic, background, seal, ghost image (e.g., a faint image),optical variable device, hologram, Kinogram®, IR or UV images, line art,biometric representation (e.g., an image of a fingerprint, iris, etc.),artwork, etc, which are generally referred to as “artwork.” The embeddedartwork is printed on an ID document or stored in electronic oroptical/magnetic memory of an ID document. In other implementations, thefinal spatial domain minutiae information (e.g., after step 98 in FIG.11) is stored in a 2D barcode or the like. However, it is mostpreferable to steganographically embed minutiae information, e.g., in aphotograph, for added security.

An authentication procedure is discussed with reference to FIG. 12. Anembedded ID document 110 is presented to an optical scanner 120. Theembedded ID document 110 includes watermark minutiae information, asdiscussed above, steganographically embedded therein. (If thewatermarked image/graphic is stored on optical/magnetic memory orelectronic circuitry, then the ID is presented to a suitable andcorresponding reader.). The optical scanner captures image datacorresponding to the ID document 108, e.g., captures image datacorresponding to the embedded photograph 108 or a portion of thephotograph 108. The captured image data includes the embedded watermarkminutiae information.

A watermark decoder analyzes the captured image data. If needed toresolve image distortion such as rotation and scale, the watermarkdetector uses the embedded watermark orientation component to adjust theimage data to compensate for such distortion (step 122). The watermarkdetector then reverses or unfolds the watermark embedding process torecover the minutiae map in a minutiae domain. For example, if thewatermark is redundantly embedded in the image data, the watermarkdetector may accumulate image tiles (or blocks) to help improve asignal-to-noise ratio of the minutiae information (e.g., improving thesignal-to-noise relationship of the final spatial domain minutiaeinformation over the image data as in step 124). The accumulated imagedata is inversely permuted or unscrambled (step 126), according to a keyor method corresponding to the permutation used in step 98 (FIG. 11).The inversely permuted image data is then transformed (step 128), e.g.,using a corresponding transformation like a Fast Fourier Transform(FFT). Peaks or prominent locations are detected in the transform domain(step 130), which once determined, yield a minutiae map or minutiaedomain specification (hereafter interchangeably referred to as a“control minutiae map,” e.g., map 132).

A bearer (e.g., the human subject) of the ID document 110 presents herfingerprint to be sampled (step 140). This fingerprint is called a“sample fingerprint.” (The sampling preferably uses the same protocol aswas used to capture the original fingerprint. Otherwise, theauthentication system preferably includes some knowledge of how theoriginal fingerprint capture and the sample fingerprint captureprocesses differ, and may use this knowledge to compensate for anysubstantial differences. Knowledge of the original process can becarried, e.g., by a digital watermark component.). The samplefingerprint is analyzed to determine minutiae points (step 142), e.g.,using NIST's NFIS software, specifically the MINDTCT minutiae detector.The minutiae points are mapped or transformed into a minutiae domainusing the mapping procedure in step 94 (or a known variation of theprocedure) as discussed with respect to FIG. 11. A minutiae map orminutiae domain specification for the sample fingerprint results(hereafter interchangeably referred to as a “sample minutiae map,” e.g.,map 144).

To validate the ID document 110, or to validate whether the bearer ofthe ID document 110 is an authorized bearer of the ID document 110, ageneralized matching module 146 (e.g., a pattern matching module,location matching, etc.) determines whether the control minutiae map 132matches or corresponds within a predetermined tolerance to the sampleminutiae map 144. If the control and sample minutiae maps 132 and 144match or otherwise correspond, the bearer is considered an authorizedbearer of the ID document 110. Otherwise, the bearer is not consideredan authorized bearer of the ID document 110. A similar determination canbe made as to whether the ID document 110 is considered valid ortrustworthy. The matching module 146 may output such results, or mayoutput data which is used to determine such results.

Minutiae Location Matching

FIGS. 13 and 14 illustrate matching methods, which can be interchangedwith the matching module 146 shown in FIG. 12. These techniques arehelpful, e.g., when a sample minutiae map includes image distortion,such as rotation that may be introduced during image capture. If asample minutiae map includes distortion there may be times where asample minutiae map is mis-registered in comparison to a controlminutiae map. The following techniques are also helpful when a minutiaedetermining algorithm creates an error (e.g., indicating that a minutiaepoint exists when it should not exist or mis-identifies a minutiaepoint). The distortion or minutiae errors may cause erroneous result ifmatching criteria is too stringent. Thus a matching module may benefitfrom a more flexible or relaxed matching criteria.

One inventive method calculates segment lengths between minutiae pointsand matches segment lengths to determine whether a control and sampleminutiae map correspond. An outline of steps follows.

-   -   1. Line segments are constructed (perhaps virtually) between all        possible minutiae locations in a sample minutiae map (FIG. 13        a). The lengths of the line segments (e.g., in terms of pixels)        and their corresponding minutiae end points are remembered, e.g.        cached, stored or buffered, etc., in a “sample length list.”    -   2. From a control minutiae map (FIG. 13 b), two of the strongest        (or most prominent) peaks or minutiae locations are identified.        This step may optionally determine a relative listing of        locations, from strongest or most prominent locations to weakest        or less prominent locations. The list can also be truncated to        include only the top, e.g., 25-50 locations.    -   3. A segment length between the two strongest minutiae points is        determined (see FIG. 13 c), and any matching segment lengths        from the sample length list are identified.    -   4. The matching segments are added to an “Orientation Table”        (see FIG. 13 d). The orientation table is used to manage        matching segments. The table will eventually include different        entries, which are used to identify different hypothesizes for a        correct orientation for registering the sample with the control        minutiae map. To illustrate, say the strongest point from the        control minutiae map corresponds to minutiae location no. 6 and        the second strongest corresponds to minutiae location no. 10.        And say a line segment (dotted line) between minutiae locations        6 and 10 is found to correspond (e.g., within some predetermined        tolerance, perhaps even a tolerance associated with a        fingerprint capture process) to a line segment formed between        minutiae locations nos. 38 and 2 from the sample list. Then        there are two possible orientation entries between the matching        segments, as shown in FIG. 13 d. That is, control minutiae        location no. 6 may correspond to sample minutiae location no. 38        or to sample minutiae location no. 2, and control minutiae        location no. 10 may correspond with sample minutiae location no.        2 or sample minutiae location no. 38.    -   5. For each new peak locations (e.g., in order of strongest to        weakest from a relative listing of minutiae locations), a        segment length between a new location and each of the old peak        locations is determined. For example, for the 3^(rd) strongest        location, a segment length is determined between the 3^(rd) and        2^(nd) locations and the 3^(rd) and 1 ^(st) locations (see FIG.        13 e). Each segment length is compared to the sample length list        to find possible match segments.        -   a. Each matching segment is added as a new orientation. For            example, say the 3^(rd) strongest location corresponds with            control minutiae location no. 11. A length of a line segment            formed between control minutiae location no. 11 and control            minutiae location no. 10 is found to correspond to a segment            length formed between sample minutiae location nos. 38 and            29. Two new entries are added to the orientation table            (e.g., entries 3 and 4 in FIG. 13 f).        -   b. And since the table already has a “10-38” possible pair            (see entry 2 in FIG. 13 d), it is preferable to determine            whether a matching segment (e.g., 11-29) should be added to            the second entry as well. To determine whether to add            another entry, one can determine whether the lengths of            segments between candidate points coincide. For example: i)            if the length of a segment between control minutiae            locations 10 and 11 corresponds with the length of a segment            between sample minutiae locations 38 and 29; and ii) if a            length of a segment between control minutiae locations 6 and            11 corresponds with the length of a segment between sample            minutiae locations 2 and 29, then the new candidate pair            11-29 is added to the second entry (FIG. 13 f). (In general,            for any orientation in the table, we prefer that the segment            length between any two points on the control list, e.g.,            location numbers 6 and 11, match or correspond with a            distance between corresponding points on the sample list,            e.g., location numbers 2 and 29.)    -   6. Step 5 repeats for each possible line segment formed from new        minutiae locations selected in, e.g., descending order from the        list of the most relevant minutiae locations, or until a        particular table entry grows to a predetermined number of        entries, e.g., signaling a proper orientation between control        and sample minutiae locations, and perhaps a match.    -   7. A proper orientation is determined by matching or registering        corresponding points from within the largest table entry (in the        FIG. 13 f example—entry 2), and if the points match (or if a        predetermined number of the points match), the control minutiae        map is considered to correspond with the sample minutiae map.

FIG. 14 illustrates an alternative matching module/method. Here again,the matching utilizes segment lengths. An outline of step follows.

-   -   1. For each of a control minutiae map (FIG. 14 a) and a sample        minutiae map (FIG. 14 b), determine lengths for all possible        line segments in the map. That is, determine segments between        every possible minutiae location. Of course, a list of the most        relevant minutiae locations can be determined, with segments        lengths being determined between only the relevant minutiae        locations.    -   2. For each line segment determine an angle from a horizontal        that is associated with the line segment, and categorize or map        (or plot) of all the line segments according to segment length        and angle. This categorization or mapping can be referred to as        an angle map (FIGS. 14 c and 14 d).    -   3. Correlate the sample angle map with the control angle map.        For example, a shifting correlation along the angle axis should        identify a corresponding angle (or a difference in angle Δθ) in        which the two control angle maps correspond (that is, if the        sample fingerprint corresponds with the control fingerprint).        This process identifies an angle (or difference in angles),        which provides a high correlation between the two angle maps        (see FIG. 14 e).    -   4. The sample minutiae map is adjusted according to the angle        Δθ, and the control minutiae map and the sample minutes map are        then compared (e.g. point matching or segment matching) for        correspondence. Alternatively, a judgment can be based on the        angle Δθ itself; that is, if a θ is identified with at least a        predetermined correlation value, then the sample fingerprint and        the control fingerprint can be classified as matching or        otherwise corresponding. Similarly, if no angle with a        correlation value is found after correlation to meet or exceed        the predetermined correlation value, then the sample fingerprint        and the control fingerprint can be considered to not match or        otherwise not correspond.

Optionally, if the control and sample minutiae maps include minutiaeorientation information in terms of phase, the correlation angle can besubtracted from the sample map, and steps 2-4 are repeated. As a furtheroptional step, the segments as represented in an angle map areaugmented, e.g. rectangles are centered on selected segments. An areadefined by a rectangle then represents a particular segment at a givenangle. The area defined by a rectangle can be adjusted to provide a moreforgiving matching module. A correlation process then correlates areasbetween the two angle maps. Of course, the area of the rectangles can beadjusted for (or to reflect) predetermined tolerances, e.g., a segmentwith a short distance can have a relatively shorter horizontal side,while a segment with a longer distance can include a longer rectangleside. Lengthening or shortening a rectangle's vertical edges cansimilarly adjust for (or reflect an) angular tolerance. An area-basedcorrelation may be even more forgiving of minutiae calculation errorsand image distortion.

Alternatives

In an alternative implementation, an ID document photograph includes aso-called fragile watermark, which is designed to degrade when subjectedto certain types of signal processing, like scanning and printing. Thecondition (or absence) of a fragile watermark reveals whether thephotograph has been tampered with.

The ID document may alternatively include multiple watermarks, which canbe cross-correlated to detect tampering. For example, the photographincludes a first watermark—perhaps a message component that isassociated with watermark minutiae information—and another portion ofthe document includes a second watermark. The first and secondwatermarks can be cross-correlated to help further authenticate the IDdocument. (See, e.g., U.S. patent application Ser. No. 10/686,495, filedOct. 14, 2004, for even further details regarding cross-correlation ofmultiple features or watermarks for validation. The above patentapplication is herein incorporated by reference.). In anotherimplementation the ID document includes two watermarks, one in the image108 and one in a background, seal, graphic, etc. Each of the watermarksmay include the minutiae information. The minutiae information betweenboth watermarks can be compared, if desired, to further enhance securityof the ID document.

Still further, a watermark may include an auxiliary data (or anidentifier)—perhaps associated with watermark minutiae information—that,once decoded, is used to interrogate a data repository to obtainauthenticating information, like a photograph of an authorized bearer ofthe ID document. The auxiliary data may be used to identify which finger(or thumb) corresponds to the control fingerprint, allowing the bearerof the ID document to present the appropriate finger for fingerprinting.Further still, an ID document may include multiple control minutiaemaps, each corresponding to different fingerprints belonging to a bearerof the ID document, or corresponding to different people who areauthorized to bear the ID document or who can be validated by the IDdocument. For example, one ID document can represent a family or group.Each member of the family or group provides a fingerprint from which acontrol minutiae map is generated. Each control minutiae is permuteddifferently to avoid interfering with the other maps. Each permuted mapis combined, perhaps with an orientation component, and is embeddedon/in the same ID document.

Also, instead of explicitly embedding fingerprint minutiae locations,our techniques can be applied to different biometric samples as well.For example, a mapping of predominate iris characteristics, handgeometries, facial patterns, etc., can be explicitly embedded in aphotograph or other artwork as well.

Concluding Remarks

Having described and illustrated the principles of the technology withreference to specific implementations, it will be recognized that thetechnology can be implemented in many other, different, forms. Toprovide a comprehensive disclosure without unduly lengthening thespecification, applicants hereby incorporate by reference each of the USpatent documents referenced above, along with Appendix A, which is arelated work of the inventors. Of course, the methods and systemsdescribed in Appendix A can be readily combined with the methods andsystems described above.

The methods, processes, and systems described above may be implementedin hardware, software or a combination of hardware and software. Forexample, a digital watermark encoding processes may be implemented in aprogrammable computer or a special purpose digital circuit. Similarly,data decoding may be implemented in software, firmware, hardware, orcombinations of software, firmware and hardware. The other methods andprocesses described above may be implemented in programs executed from asystem's memory (a computer readable medium, such as an electronic,optical or magnetic storage device or removable or mass memory), or fromhardware/software combinations.

The particular combinations of elements and features in theabove-detailed embodiments are exemplary only; the interchanging andsubstitution of these teachings with other teachings in this and theincorporated-by-reference patents/applications are also expresslycontemplated.

Appendix A Statistical Quality Assessment of Fingerprint 1. Introduction

The quality of a fingerprint is essential to the performance of AFIS(Automatic Fingerprint Identification System). Such a quality may beclassified by clarity and regularity of ridge-valley structures.^(1, 2)One may calculate thickness of ridge and valley to measure the clarityand regularity. However, calculating a thickness is not feasible in apoor quality image, especially, severely damaged images that containbroken ridges (or valleys). In order to overcome such a difficultly, theproposed approach employs the statistical properties in a local block,which involve the mean and spread of the thickness of both ridge andvalley. The mean value used for determining whether a fingerprint is wetor dry. For example, the black pixels are dominant if a fingerprint iswet, the average thickness of ridge is larger than one of valley, andvice versa on a dry fingerprint. In addition, a standard deviation isused for determining severity of damage. In this study, the quality isdivided into three categories based on two statistical propertiesmentioned above: wet, good and dry. The number of low quality blocks isused to measure a global quality of fingerprint. In addition, adistribution of poor blocks is also measured using Euclidean distancebetween groups of poor blocks. With this scheme, locally condensed poorblocks decreases the overall quality of an image. Experimental resultson the fingerprint image captured by optical devices as well as by arolling method show the wet and dry parts of image were successfullycaptured. Enhancing an image by employing morphology techniques thatmodifying the detected poor quality blocks is illustrated in section 3.

2. Method

2.1 Block Orientation

A fingerprint image is first divided into small blocks and anorientation of each Block is calculated using a global directional map.That map is derived by averaging quantized angles at all pixels within ablock, where the angle at each pixel is the angle of slit that has themaximum values after measuring total pixel values with eight directionalslits separated by π/8. The detail angle in each block is then obtainedby changing a block orientation to a small degree to both clockwise andcounterclockwise. The best angle is obtained when such an angle achievesthe maximum column sum. Both global and detail angel calculation methodsare based on the methods employed in NIST fingerprint image software.³Examples of a global direction map and block rotations are illustratedin FIG. 1( b) and (c).

2.2 Thickness Calculation

Subsequently, the ridge lines in a rotated block are scanned in thedirection normal to the ridge flow to measure the thickness of black andwhite lines, where the first and the last values in a line scan areignored due to the possible incomplete lines. Noise removal filters areemployed before and after the thickness calculation. A 1-D median filteralong a ridge orientation (vertically) reduces noise that was amplifiedby rotation and the 5% of both high and low measured data are discarded.FIG. 1.(d) shows measured thickness in terms of the number of pixels forthe rotated B/W block in FIG. 1.(c), where ridge and valley arerepresented by ‘+’ and ‘o’ respectively. The high values of ‘o’ markindicates that ridge lines are broken.

2.3 Local Quality Assessment

The two statistical measurements, mean and standard deviation, arecalculated from collected data in each block. Those statistics determinethe quality of local block that is classified into three categories:wet, good and dry. If a finger is wet, where the black pixels aredominate, the average thickness of ridge (μb) is larger than that ofvalley (μw). The opposite result is acquired if a finger is dry. Veryhigh value of either μw or μb is the indication of poor quality. Furtheranalysis for severity is performed with a spread (σ) that represents theclarity of lines. A good fingerprint image tends to have a smalldeviation value for both ridge and valley. However, if a finger is dry,the deviation of valley thickness is larger than one of ridge thicknessdue to thin and broken black lines. ON the other hand, the spread ofridge is significantly large is the case of wet fingerprint. TypicalGaussian distributions of such categories are shown in FIG. 2.(c), FIG.3.(c), and FIG. 4.(c), where measurement of ridge and valley arerepresented by dotted and solid lines respectively. The statisticalproperties in Table 1 are used for classifying a quality, where the B/Wration is the ration of number of black and white pixels.

TABLE 1 Statistical properties for quality decision Dry Wet μb-μw  <0 >0 σw high low σb low high B/W Ratio <<1 >>12.4 Global Quality Assessment

The number of low quality blocks can be used to assess the globalquality of a fingerprint. In other words, the quality is measured bydividing total number of low quality blocks. And the severity of drynessand wetness are calculated by considering only dry and wet blocks, DryIndex (DI)=Nd/Nt and Wet Index (WI) Nv/Nt, where Nd, Nw, and Nt arenumber of dry blocks, number of wet blocks, and number of total blocksrespectively.

In addition, the distribution of poor blocks is also taken intoconsideration for a global quality. With this scheme, if poor qualityblocks are locally condensed, the quality of that image is consideredlower than one that has dispersed poor blocks. This property is measuredby Cluster Index (CI) using Euclidean distances between groups of poorblocks.

3. Results and Conclusions

The proposed approach was tested on the fingerprint image captured byoptical devices as well as by a rolling method captured by DMV. The sizeof image and a local block were [512×512] and [24×24] respectively.Typical results shown in FIG. 5 clearly shows the wet and dry block s ofimage were successfully captured.

Detected local blocks that were classified as dry or wet, can bemodified using a morphology technique.⁴ This technique reduces orexpands the thickness or valley. Example shown in FIG. 6 illustratedthat the morphology that were applied only on the dry blocks reduced thenumber of dry blocks, while that of wet blocks (marked as ‘x’) wereunchanged. The detailed views of previous and after operation are shownin FIG. 6.(d) and (e).

FIG. 7.(a) shows Dry Index (DI) on 31 data samples captured using anoptical fingerprint scanner, where x-axis and y-axis represent dataindex and DI respectively. Dryness of 7^(th) and 31^(st) sample wereclose by observing DI: 0.148 and 0.155. However, the distribution of dryblocks were significantly different as illustrated in FIG. 8 and FIG. 9.The dry blocks are locally condensed at the lower right in the formerfigure, while those are dispersed in the latter. Such a distinction waswell represented with Cluster Index calculated with groups of dry blocksthat are marked in FIG. 8.(c) and FIG. 9.(c), where the CIs on the same31 samples are plotted in FIG. 7.(b) in log scale.

REFERENCES

-   -   1. E. Lim, X. Jian, and W. Yau, “Fingerprint quality and        validity analysis,” IEEE ICIP, 2002.    -   2. L. Hong, Y. Wan and A. Jain, “Fingerprint image enhancement:        Algorithm and performance evaluation,” IEEE Transactions on        Pattern Analysis and Machine Intelligence, vol. 20, 777-789,        1998.    -   3. NFIS, NIST Fingerprint Image Software, 10, 2001.    -   4. R. G. Gonzalez and R. E. Woods, Digital Image Processing,        Addison-Wesley Publishing Company, 1992.

1. A method to analyze a fingerprint image corresponding to a humanfingerprint of a human subject, the fingerprint image including ridgesand valleys, said method comprising: receiving a fingerprint imagecorresponding to the human fingerprint of the human subject; determiningimage distortion associated with the fingerprint image; adjusting thefingerprint image to compensate for the image distortion; segmenting thefingerprint image into at least a plurality of subsets; and for each ofthe subsets: determining a measure associated with fingerprint ridgesand fingerprint valleys in the subset; and based on the measureassigning the subset to at least one of a plurality of categories;assessing an overall acceptability of the fingerprint image by analyzingat least one of the plurality of categories, and requiring capture ofanother fingerprint image from the human subject if the overallacceptability of the fingerprint image is at or below predeterminedcriteria.
 2. The method of claim 1 wherein said analyzing comprisesdetermining whether a predetermined number of subsets are assigned to afirst of the plurality of categories.
 3. The method of claim 2, whereinthe first category comprises at least one of acceptable, wet or dry. 4.The method of claim 2, further comprising accepting the fingerprintimage when a predetermined number of subsets are assigned to the firstof the plurality of categories.
 5. The method of claim 2 furthercomprising rejecting the fingerprint image when a predetermined numberof subsets are not assigned to the first of the plurality of categories.6. The method of claim 1, wherein the measure comprises a statisticalmeasure.
 7. The method of claim 1, wherein said determining imagedistortion associated with the image data comprises determining a globalorientation.
 8. The method of claim 7, wherein said determining imagedistortion associated with the image data further comprises determiningan orientation relative to an image subset.
 9. The method of claim 1,wherein the plurality of subsets comprises less image data than thereceived fingerprint image.
 10. The method of claim 1, furthercomprising filtering the image to remove noise.
 11. The method of claim1, further comprising modifying image data defined by a subset when thesubset is assigned to a predetermined category.
 12. The method of claim11, wherein the modifying comprises adjusting ridge or valleydimensions.
 13. A method of generating an identification documentincluding a fingerprint image comprising: receiving a fingerprint imagethat has been analyzed according to claim 1; and printing thefingerprint image on the identification document or storing thefingerprint image in memory provided by the identification document. 14.The method of claim 13, wherein the memory comprises at least one ofelectronic memory circuits, optical memory or magnetic memory.
 15. Amethod to analyze a fingerprint image, the fingerprint image includingridges and valleys, said method comprising: receiving a fingerprintimage corresponding to a human fingerprint; determining image distortionassociated with the fingerprint image; adjusting the fingerprint imageto compensate for the image distortion; segmenting the fingerprint imageinto at least a plurality of subsets; for each of the subsets:determining a measure associated with fingerprint ridges and fingerprintvalleys in the subset; and based on the measure assigning the subset toat least one of a plurality of categories; determining which of thesubsets correspond to a fingerprint core, and weighting those subsetsthat correspond to the fingerprint core more significantly relative tothose subsets that do not correspond to the fingerprint core; andassessing an overall acceptability of the fingerprint image by analyzingat least one of the plurality of categories.
 16. A method to analyze afingerprint image, the fingerprint image including ridges and valleys,said method comprising: receiving a fingerprint image corresponding to ahuman fingerprint; determining image distortion associated with thefingerprint image; adjusting the fingerprint image to compensate for theimage distortion; segmenting the fingerprint image into at least aplurality of subsets; for each of the subsets: determining a statisticalmeasure associated with fingerprint ridges and fingerprint valleys inthe subset, wherein the statistical measure comprises at least astandard deviation of ridge thickness and a standard deviation of valleythickness; and based on the statistical measure assigning the subset toat least one of a plurality of categories.
 17. The method of claim 16,wherein the statistical measure further comprises at least one of meanof ridge thickness or mean of valley thickness.
 18. A method ofdetermining whether to accept a first fingerprint image or to requirecapture of a second fingerprint image, the first fingerprint and secondfingerprint images comprising a representation of a same humanfingerprint, the fingerprint including ridges and valleys, said methodcomprising: determining an orientation of the first fingerprint image;adjusting the fingerprint image according to the orientation;determining statistical values that are associated with at least some ofthe ridges and valleys; comparing the determined statistical valuesagainst predetermined acceptance criteria; and determining whether toaccept the first fingerprint image based at least on the comparison. 19.The method of claim 18, wherein statistics are determined for selectedimage areas.
 20. The method of claim 18, wherein the statistical valuesare determined based on ridge and valley thickness.
 21. The method ofclaim 18, wherein the statistical values are determined based on arelationship between the ridges and valleys.
 22. The method of claim 18,wherein statistical values associated with a fingerprint core areconsidered to be more significant than statistical values that are notassociated with the fingerprint core.
 23. The method of claim 18,wherein the statistical values comprise a measure related to a frequencydomain analysis of at least some of the ridges and valleys.
 24. A methodof generating an identification document including a fingerprint imagecomprising: receiving a fingerprint image that has been processedaccording to claim 18; and printing the fingerprint image on theidentification document or storing the fingerprint image in memoryprovided by the identification document.
 25. A method to analyze imagedata corresponding to a human fingerprint, the human fingerprintincluding fingerprint ridges and valleys, said method comprising:receiving image data corresponding to a human fingerprint; determiningimage distortion associated with the image data; adjusting thefingerprint image to compensate for the image distortion; segmenting thefingerprint image into at least a plurality of subsets; and for each ofthe subsets: determining a measure associated with the human fingerprintin the subset; and based on the measure assigning the subset to at leastone of a plurality of categories; determining acceptability of the humanfingerprint at least through use of or reference to at least one of theplurality of categories, and requiring recapture of the humanfingerprint if the acceptability of the human fingerprint is at or belowpredetermined criteria.
 26. A computer readable medium comprisinginstructions or circuitry to perform the method of claim
 25. 27. Amethod of determining whether to accept a first fingerprint image or torequire capture of a second fingerprint image, the first fingerprint andsecond fingerprint images comprising a same representation of a humanfingerprint, the fingerprint including ridges and valleys, said methodcomprising: determining an orientation of the first fingerprint image;adjusting the fingerprint image according to the orientation;determining statistical values that are associated with at least aportion of the human fingerprint; comparing the determined statisticalvalues against predetermined acceptance criteria; and determiningwhether to accept the first fingerprint image based at least in part onthe comparison.